Development and validation of a rapid and robust method to determine visceral adipose tissue volume using computed tomography images
Visceral adiposity is a risk factor for many chronic diseases. Existing methods to quantify visceral adipose tissue volume using computed tomographic (CT) images often use a single slice, are manual, and are time consuming, making them impractical for large population studies. We developed and valid...
Gespeichert in:
| Veröffentlicht in: | PloS one 2017-08, Vol.12 (8), p.e0183515-e0183515 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | e0183515 |
|---|---|
| container_issue | 8 |
| container_start_page | e0183515 |
| container_title | PloS one |
| container_volume | 12 |
| creator | Parikh, Aaroh M Coletta, Adriana M Yu, Z Henry Rauch, Gaiane M Cheung, Joey P Court, Laurence E Klopp, Ann H |
| description | Visceral adiposity is a risk factor for many chronic diseases. Existing methods to quantify visceral adipose tissue volume using computed tomographic (CT) images often use a single slice, are manual, and are time consuming, making them impractical for large population studies. We developed and validated a method to accurately, rapidly, and robustly measure visceral adipose tissue volume using CT images.
In-house software, Medical Executable for the Efficient and Robust Quantification of Adipose Tissue (MEERQAT), was developed to calculate visceral adipose tissue volume using a series of CT images within a manually identified region of interest. To distinguish visceral and subcutaneous adipose tissue, ellipses are drawn through the rectus abdominis and transverse abdominis using manual and automatic processes. Visceral and subcutaneous adipose tissue volumes are calculated by counting the numbers of voxels corresponding to adipose tissue in the region of interest. MEERQAT's ellipse interpolation method was validated by comparing visceral adipose volume from 10 patients' CT scans with corresponding results from manually delineated scans. Accuracy of visceral adipose quantification was tested using a phantom consisting of animal fat and tissues. Robustness of the method was tested by determining intra-observer and inter-observer coefficients of variation (CV).
The mean difference in visceral adipose tissue volume between manual and elliptical delineation methods was -0.54 ± 4.81%. In the phantom, our measurement differed from the known adipose volume by ≤ 7.5% for all scanning parameters. Mean inter-observer CV for visceral adipose tissue volume was 0.085, and mean intra-observer CV for visceral adipose tissue volume was 0.059.
We have developed and validated a robust method of accurately and quickly determining visceral adipose tissue volume in any defined region of interest using CT imaging. |
| doi_str_mv | 10.1371/journal.pone.0183515 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_1934236531</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A502599774</galeid><doaj_id>oai_doaj_org_article_65a8e0c94ab94a04bb72a6fb8d0cb78f</doaj_id><sourcerecordid>A502599774</sourcerecordid><originalsourceid>FETCH-LOGICAL-c692t-908cd749004da635afb56c6cffe53820d965b1106fee8bfb46197ba6a9515acb3</originalsourceid><addsrcrecordid>eNqNk01r3DAQhk1padK0_6C0gkJpD7uVbEu2L4WQfi0EAv26ipE09irYlmvJS3PvD6-8uwm7JYdijMXomVczrzVJ8pzRJcsK9u7aTWMP7XJwPS4pKzPO-IPklFVZuhApzR4erE-SJ95fU8qzUojHyUlalrxijJ8mfz7gBls3dNgHAr0hG2itgWBdT1xNgIwwWLPdGZ2afCAdhrUzJDhiMODY2R7JxnqNI7QEjB2cRxKs91OMu3bqkEze9g3RrhumgHNq55qou74htoMG_dPkUQ2tx2f771ny49PH7xdfFpdXn1cX55cLLao0LCpaalPkFaW5AZFxqBUXWui6xthYSk0luGKMihqxVLXKBasKBQKqaA1olZ0lL3e6Q-u83BvoZbQpTzPBMxaJ1Y4wDq7lMMb6xhvpwMptwI2NhDFY3aIUHEqkuspBxZfmShUpiFqVhmpVlHXUer8_bVIdGh0djhYdiR7v9HYtG7eRnBeloEUUeLMXGN2vCX2Q3exz20KPbprrzqMVRbGt-9U_6P3d7akGYgO2r108V8-i8pzTlFdVUeSRWt5DxcdgZ3W8bbWN8aOEt0cJkQn4OzQweS9X377-P3v185h9fcCuEdqw9vFGzZfTH4P5DtSj837E-s5kRuU8LLduyHlY5H5YYtqLwx90l3Q7HdlfmLUSZA</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1934236531</pqid></control><display><type>article</type><title>Development and validation of a rapid and robust method to determine visceral adipose tissue volume using computed tomography images</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Public Library of Science (PLoS) Journals Open Access</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Parikh, Aaroh M ; Coletta, Adriana M ; Yu, Z Henry ; Rauch, Gaiane M ; Cheung, Joey P ; Court, Laurence E ; Klopp, Ann H</creator><contributor>Gonzalez-Bulnes, Antonio</contributor><creatorcontrib>Parikh, Aaroh M ; Coletta, Adriana M ; Yu, Z Henry ; Rauch, Gaiane M ; Cheung, Joey P ; Court, Laurence E ; Klopp, Ann H ; Gonzalez-Bulnes, Antonio</creatorcontrib><description>Visceral adiposity is a risk factor for many chronic diseases. Existing methods to quantify visceral adipose tissue volume using computed tomographic (CT) images often use a single slice, are manual, and are time consuming, making them impractical for large population studies. We developed and validated a method to accurately, rapidly, and robustly measure visceral adipose tissue volume using CT images.
In-house software, Medical Executable for the Efficient and Robust Quantification of Adipose Tissue (MEERQAT), was developed to calculate visceral adipose tissue volume using a series of CT images within a manually identified region of interest. To distinguish visceral and subcutaneous adipose tissue, ellipses are drawn through the rectus abdominis and transverse abdominis using manual and automatic processes. Visceral and subcutaneous adipose tissue volumes are calculated by counting the numbers of voxels corresponding to adipose tissue in the region of interest. MEERQAT's ellipse interpolation method was validated by comparing visceral adipose volume from 10 patients' CT scans with corresponding results from manually delineated scans. Accuracy of visceral adipose quantification was tested using a phantom consisting of animal fat and tissues. Robustness of the method was tested by determining intra-observer and inter-observer coefficients of variation (CV).
The mean difference in visceral adipose tissue volume between manual and elliptical delineation methods was -0.54 ± 4.81%. In the phantom, our measurement differed from the known adipose volume by ≤ 7.5% for all scanning parameters. Mean inter-observer CV for visceral adipose tissue volume was 0.085, and mean intra-observer CV for visceral adipose tissue volume was 0.059.
We have developed and validated a robust method of accurately and quickly determining visceral adipose tissue volume in any defined region of interest using CT imaging.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0183515</identifier><identifier>PMID: 28859115</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adipose tissue ; Aged ; Aged, 80 and over ; Animal fat ; Biology and Life Sciences ; Body fat ; Body mass index ; CAT scans ; Chronic diseases ; Chronic illnesses ; Coefficient of variation ; Computation ; Computed tomography ; Computer and Information Sciences ; Delineation ; Ellipses ; Female ; Health risks ; Humans ; Image processing ; Image Processing, Computer-Assisted - methods ; Interpolation ; Intra-Abdominal Fat - diagnostic imaging ; Intra-Abdominal Fat - physiopathology ; Mathematical analysis ; Medical imaging ; Medicine and Health Sciences ; Methods ; Middle Aged ; Obesity ; Obesity, Abdominal - diagnosis ; Obesity, Abdominal - diagnostic imaging ; Obesity, Abdominal - physiopathology ; Phantoms, Imaging ; Physical Sciences ; Physiological aspects ; Population studies ; Research and Analysis Methods ; Risk factors ; Robustness ; Scanning ; Software ; Subcutaneous Fat - diagnostic imaging ; Subcutaneous Fat - physiopathology ; Tissues ; Tomography, X-Ray Computed - methods</subject><ispartof>PloS one, 2017-08, Vol.12 (8), p.e0183515-e0183515</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Parikh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2017 Parikh et al 2017 Parikh et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-908cd749004da635afb56c6cffe53820d965b1106fee8bfb46197ba6a9515acb3</citedby><cites>FETCH-LOGICAL-c692t-908cd749004da635afb56c6cffe53820d965b1106fee8bfb46197ba6a9515acb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5578607/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5578607/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28859115$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Gonzalez-Bulnes, Antonio</contributor><creatorcontrib>Parikh, Aaroh M</creatorcontrib><creatorcontrib>Coletta, Adriana M</creatorcontrib><creatorcontrib>Yu, Z Henry</creatorcontrib><creatorcontrib>Rauch, Gaiane M</creatorcontrib><creatorcontrib>Cheung, Joey P</creatorcontrib><creatorcontrib>Court, Laurence E</creatorcontrib><creatorcontrib>Klopp, Ann H</creatorcontrib><title>Development and validation of a rapid and robust method to determine visceral adipose tissue volume using computed tomography images</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Visceral adiposity is a risk factor for many chronic diseases. Existing methods to quantify visceral adipose tissue volume using computed tomographic (CT) images often use a single slice, are manual, and are time consuming, making them impractical for large population studies. We developed and validated a method to accurately, rapidly, and robustly measure visceral adipose tissue volume using CT images.
In-house software, Medical Executable for the Efficient and Robust Quantification of Adipose Tissue (MEERQAT), was developed to calculate visceral adipose tissue volume using a series of CT images within a manually identified region of interest. To distinguish visceral and subcutaneous adipose tissue, ellipses are drawn through the rectus abdominis and transverse abdominis using manual and automatic processes. Visceral and subcutaneous adipose tissue volumes are calculated by counting the numbers of voxels corresponding to adipose tissue in the region of interest. MEERQAT's ellipse interpolation method was validated by comparing visceral adipose volume from 10 patients' CT scans with corresponding results from manually delineated scans. Accuracy of visceral adipose quantification was tested using a phantom consisting of animal fat and tissues. Robustness of the method was tested by determining intra-observer and inter-observer coefficients of variation (CV).
The mean difference in visceral adipose tissue volume between manual and elliptical delineation methods was -0.54 ± 4.81%. In the phantom, our measurement differed from the known adipose volume by ≤ 7.5% for all scanning parameters. Mean inter-observer CV for visceral adipose tissue volume was 0.085, and mean intra-observer CV for visceral adipose tissue volume was 0.059.
We have developed and validated a robust method of accurately and quickly determining visceral adipose tissue volume in any defined region of interest using CT imaging.</description><subject>Adipose tissue</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Animal fat</subject><subject>Biology and Life Sciences</subject><subject>Body fat</subject><subject>Body mass index</subject><subject>CAT scans</subject><subject>Chronic diseases</subject><subject>Chronic illnesses</subject><subject>Coefficient of variation</subject><subject>Computation</subject><subject>Computed tomography</subject><subject>Computer and Information Sciences</subject><subject>Delineation</subject><subject>Ellipses</subject><subject>Female</subject><subject>Health risks</subject><subject>Humans</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Interpolation</subject><subject>Intra-Abdominal Fat - diagnostic imaging</subject><subject>Intra-Abdominal Fat - physiopathology</subject><subject>Mathematical analysis</subject><subject>Medical imaging</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Obesity</subject><subject>Obesity, Abdominal - diagnosis</subject><subject>Obesity, Abdominal - diagnostic imaging</subject><subject>Obesity, Abdominal - physiopathology</subject><subject>Phantoms, Imaging</subject><subject>Physical Sciences</subject><subject>Physiological aspects</subject><subject>Population studies</subject><subject>Research and Analysis Methods</subject><subject>Risk factors</subject><subject>Robustness</subject><subject>Scanning</subject><subject>Software</subject><subject>Subcutaneous Fat - diagnostic imaging</subject><subject>Subcutaneous Fat - physiopathology</subject><subject>Tissues</subject><subject>Tomography, X-Ray Computed - methods</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk01r3DAQhk1padK0_6C0gkJpD7uVbEu2L4WQfi0EAv26ipE09irYlmvJS3PvD6-8uwm7JYdijMXomVczrzVJ8pzRJcsK9u7aTWMP7XJwPS4pKzPO-IPklFVZuhApzR4erE-SJ95fU8qzUojHyUlalrxijJ8mfz7gBls3dNgHAr0hG2itgWBdT1xNgIwwWLPdGZ2afCAdhrUzJDhiMODY2R7JxnqNI7QEjB2cRxKs91OMu3bqkEze9g3RrhumgHNq55qou74htoMG_dPkUQ2tx2f771ny49PH7xdfFpdXn1cX55cLLao0LCpaalPkFaW5AZFxqBUXWui6xthYSk0luGKMihqxVLXKBasKBQKqaA1olZ0lL3e6Q-u83BvoZbQpTzPBMxaJ1Y4wDq7lMMb6xhvpwMptwI2NhDFY3aIUHEqkuspBxZfmShUpiFqVhmpVlHXUer8_bVIdGh0djhYdiR7v9HYtG7eRnBeloEUUeLMXGN2vCX2Q3exz20KPbprrzqMVRbGt-9U_6P3d7akGYgO2r108V8-i8pzTlFdVUeSRWt5DxcdgZ3W8bbWN8aOEt0cJkQn4OzQweS9X377-P3v185h9fcCuEdqw9vFGzZfTH4P5DtSj837E-s5kRuU8LLduyHlY5H5YYtqLwx90l3Q7HdlfmLUSZA</recordid><startdate>20170831</startdate><enddate>20170831</enddate><creator>Parikh, Aaroh M</creator><creator>Coletta, Adriana M</creator><creator>Yu, Z Henry</creator><creator>Rauch, Gaiane M</creator><creator>Cheung, Joey P</creator><creator>Court, Laurence E</creator><creator>Klopp, Ann H</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20170831</creationdate><title>Development and validation of a rapid and robust method to determine visceral adipose tissue volume using computed tomography images</title><author>Parikh, Aaroh M ; Coletta, Adriana M ; Yu, Z Henry ; Rauch, Gaiane M ; Cheung, Joey P ; Court, Laurence E ; Klopp, Ann H</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-908cd749004da635afb56c6cffe53820d965b1106fee8bfb46197ba6a9515acb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adipose tissue</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Animal fat</topic><topic>Biology and Life Sciences</topic><topic>Body fat</topic><topic>Body mass index</topic><topic>CAT scans</topic><topic>Chronic diseases</topic><topic>Chronic illnesses</topic><topic>Coefficient of variation</topic><topic>Computation</topic><topic>Computed tomography</topic><topic>Computer and Information Sciences</topic><topic>Delineation</topic><topic>Ellipses</topic><topic>Female</topic><topic>Health risks</topic><topic>Humans</topic><topic>Image processing</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Interpolation</topic><topic>Intra-Abdominal Fat - diagnostic imaging</topic><topic>Intra-Abdominal Fat - physiopathology</topic><topic>Mathematical analysis</topic><topic>Medical imaging</topic><topic>Medicine and Health Sciences</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Obesity</topic><topic>Obesity, Abdominal - diagnosis</topic><topic>Obesity, Abdominal - diagnostic imaging</topic><topic>Obesity, Abdominal - physiopathology</topic><topic>Phantoms, Imaging</topic><topic>Physical Sciences</topic><topic>Physiological aspects</topic><topic>Population studies</topic><topic>Research and Analysis Methods</topic><topic>Risk factors</topic><topic>Robustness</topic><topic>Scanning</topic><topic>Software</topic><topic>Subcutaneous Fat - diagnostic imaging</topic><topic>Subcutaneous Fat - physiopathology</topic><topic>Tissues</topic><topic>Tomography, X-Ray Computed - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Parikh, Aaroh M</creatorcontrib><creatorcontrib>Coletta, Adriana M</creatorcontrib><creatorcontrib>Yu, Z Henry</creatorcontrib><creatorcontrib>Rauch, Gaiane M</creatorcontrib><creatorcontrib>Cheung, Joey P</creatorcontrib><creatorcontrib>Court, Laurence E</creatorcontrib><creatorcontrib>Klopp, Ann H</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Parikh, Aaroh M</au><au>Coletta, Adriana M</au><au>Yu, Z Henry</au><au>Rauch, Gaiane M</au><au>Cheung, Joey P</au><au>Court, Laurence E</au><au>Klopp, Ann H</au><au>Gonzalez-Bulnes, Antonio</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development and validation of a rapid and robust method to determine visceral adipose tissue volume using computed tomography images</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2017-08-31</date><risdate>2017</risdate><volume>12</volume><issue>8</issue><spage>e0183515</spage><epage>e0183515</epage><pages>e0183515-e0183515</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Visceral adiposity is a risk factor for many chronic diseases. Existing methods to quantify visceral adipose tissue volume using computed tomographic (CT) images often use a single slice, are manual, and are time consuming, making them impractical for large population studies. We developed and validated a method to accurately, rapidly, and robustly measure visceral adipose tissue volume using CT images.
In-house software, Medical Executable for the Efficient and Robust Quantification of Adipose Tissue (MEERQAT), was developed to calculate visceral adipose tissue volume using a series of CT images within a manually identified region of interest. To distinguish visceral and subcutaneous adipose tissue, ellipses are drawn through the rectus abdominis and transverse abdominis using manual and automatic processes. Visceral and subcutaneous adipose tissue volumes are calculated by counting the numbers of voxels corresponding to adipose tissue in the region of interest. MEERQAT's ellipse interpolation method was validated by comparing visceral adipose volume from 10 patients' CT scans with corresponding results from manually delineated scans. Accuracy of visceral adipose quantification was tested using a phantom consisting of animal fat and tissues. Robustness of the method was tested by determining intra-observer and inter-observer coefficients of variation (CV).
The mean difference in visceral adipose tissue volume between manual and elliptical delineation methods was -0.54 ± 4.81%. In the phantom, our measurement differed from the known adipose volume by ≤ 7.5% for all scanning parameters. Mean inter-observer CV for visceral adipose tissue volume was 0.085, and mean intra-observer CV for visceral adipose tissue volume was 0.059.
We have developed and validated a robust method of accurately and quickly determining visceral adipose tissue volume in any defined region of interest using CT imaging.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28859115</pmid><doi>10.1371/journal.pone.0183515</doi><tpages>e0183515</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2017-08, Vol.12 (8), p.e0183515-e0183515 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1934236531 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS) Journals Open Access; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adipose tissue Aged Aged, 80 and over Animal fat Biology and Life Sciences Body fat Body mass index CAT scans Chronic diseases Chronic illnesses Coefficient of variation Computation Computed tomography Computer and Information Sciences Delineation Ellipses Female Health risks Humans Image processing Image Processing, Computer-Assisted - methods Interpolation Intra-Abdominal Fat - diagnostic imaging Intra-Abdominal Fat - physiopathology Mathematical analysis Medical imaging Medicine and Health Sciences Methods Middle Aged Obesity Obesity, Abdominal - diagnosis Obesity, Abdominal - diagnostic imaging Obesity, Abdominal - physiopathology Phantoms, Imaging Physical Sciences Physiological aspects Population studies Research and Analysis Methods Risk factors Robustness Scanning Software Subcutaneous Fat - diagnostic imaging Subcutaneous Fat - physiopathology Tissues Tomography, X-Ray Computed - methods |
| title | Development and validation of a rapid and robust method to determine visceral adipose tissue volume using computed tomography images |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-22T21%3A48%3A52IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Development%20and%20validation%20of%20a%20rapid%20and%20robust%20method%20to%20determine%20visceral%20adipose%20tissue%20volume%20using%20computed%20tomography%20images&rft.jtitle=PloS%20one&rft.au=Parikh,%20Aaroh%20M&rft.date=2017-08-31&rft.volume=12&rft.issue=8&rft.spage=e0183515&rft.epage=e0183515&rft.pages=e0183515-e0183515&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0183515&rft_dat=%3Cgale_plos_%3EA502599774%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1934236531&rft_id=info:pmid/28859115&rft_galeid=A502599774&rft_doaj_id=oai_doaj_org_article_65a8e0c94ab94a04bb72a6fb8d0cb78f&rfr_iscdi=true |